We consider a nonlinear function-on-function additive regression model with multiple functional predictors. The forms of the nonlinear functions are unspecified, and offer great flexibility to model ...

Nonlinear mixed effects models (NLMMs) and self-modeling nonlinear regression (SEMOR) models are often used to fit repeated measures data. They use a common function shared by all subjects to model ...

A pruning reservoir computing technique can mitigate noise and reconstruct nonlinear dynamics for potential engineering and ...

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...

Getting an all-optical AI to handle non-linear math Instead of sensing photons and processing the results, why not process the photons?

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

Highly reduced order models with nonlinear contact – extremely efficient computation. Modeled as linear components interconnected with highly reduced-order components.

Recognizing the nonlinear behavior of the 12AX7 vacuum tube, used in audio and old op-amp applications, Dr. Dyer compares various models for fit to real-world device data.

Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally ...